ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Hemichordate cis-regulatory genomics and the gene expression dynamics of deuterostomes

Deuterostomes are one major group of bilaterians composed by hemichordates and echinoderms (collectively called Ambulacraria) and chordates. Comparative studies between these groups can provide valuable insights into the nature of the last common ancestor of deuterostomes and that of bilaterians. Indirect development of hemichordates, with larval phases similar to echinoderms and an adult body plan with an anteroposterior polarity like chordates and other bilaterians, makes them a suitable model for studying the molecular basis of development among deuterostomes. However, a comprehensive, quantitative catalogue of gene expression and chromatin dynamics in hemichordates is still lacking. In this study, we analysed the transcriptomes and chromatin accessibility of multiple developmental stages of the indirect-developing hemichordate Ptychodera flava. We observed that P. flava development is underpinned by a biphasic transcriptional program probably controlled by distinct genetic networks. Comparisons with other bilaterian species revealed similar transcriptional and regulatory dynamics during hemichordate gastrulation, cephalochordate neurulation and elongation stages of annelids. By means of regulatory networks analysis and functional validations by transgenesis experiments in echinoderms, we propose that gastrulation is the stage of highest molecular resemblance in deuterostomes and that much of the molecular basis of deuterostome development was probably present in the bilaterian last common ancestor.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

Transcriptional and epigenetic characterization of a new in vitro platform to model the formation of human pharyngeal endoderm

BACKGROUND

The Pharyngeal Endoderm (PE) is an extremely relevant developmental tissue, serving as the progenitor for the esophagus, parathyroids, thyroids, lungs, and thymus. While several studies have highlighted the importance of PE cells, a detailed transcriptional and epigenetic characterization of this important developmental stage is still missing, especially in humans, due to technical and ethical constraints pertaining to its early formation.

RESULTS

Here we fill this knowledge gap by developing an in vitro protocol for the derivation of PE-like cells from human Embryonic Stem Cells (hESCs) and by providing an integrated multi-omics characterization. Our PE-like cells robustly express PE markers and are transcriptionally homogenous and similar to in vivo mouse PE cells. In addition, we define their epigenetic landscape and dynamic changes in response to Retinoic Acid by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of multiple high-throughput datasets leads to the identification of new putative regulatory regions and to the inference of a Retinoic Acid-centered transcription factor network orchestrating the development of PE-like cells.

CONCLUSIONS

By combining hESCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and the modeling of their developmental defects in genetic syndromes.

Epigenome editing technologies for discovery and medicine

Epigenome editing has rapidly evolved in recent years, with diverse applications that include elucidating gene regulation mechanisms, annotating coding and noncoding genome functions and programming cell state and lineage specification. Importantly, given the ubiquitous role of epigenetics in complex phenotypes, epigenome editing has unique potential to impact a broad spectrum of diseases. By leveraging powerful DNA-targeting technologies, such as CRISPR, epigenome editing exploits the heritable and reversible mechanisms of epigenetics to alter gene expression without introducing DNA breaks, inducing DNA damage or relying on DNA repair pathways.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Deciphering lineage specification during early embryogenesis in mouse gastruloids using multilayered proteomics

Gastrulation is a critical stage in embryonic development during which the germ layers are established. Advances in sequencing technologies led to the identification of gene regulatory programs that control the emergence of the germ layers and their derivatives. However, proteome-based studies of early mammalian development are scarce. To overcome this, we utilized gastruloids and a multilayered mass spectrometry-based proteomics approach to investigate the global dynamics of (phospho) protein expression during gastruloid differentiation. Our findings revealed many proteins with temporal expression and unique expression profiles for each germ layer, which we also validated using single-cell proteomics technology. Additionally, we profiled enhancer interaction landscapes using P300 proximity labeling, which revealed numerous gastruloid-specific transcription factors and chromatin remodelers. Subsequent degron-based perturbations combined with single-cell RNA sequencing (scRNA-seq) identified a critical role for ZEB2 in mouse and human somitogenesis. Overall, this study provides a rich resource for developmental and synthetic biology communities endeavoring to understand mammalian embryogenesis.

Multiomic profiling of transcription factor binding and function in human brain

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

The transcription factor ZNF469 regulates collagen production in liver fibrosis

Non-alcoholic fatty liver disease (NAFLD) - characterized by excess accumulation of fat in the liver - now affects one third of the world's population. As NAFLD progresses, extracellular matrix components including collagen accumulate in the liver causing tissue fibrosis, a major determinant of disease severity and mortality. To identify transcriptional regulators of fibrosis, we computationally inferred the activity of transcription factors (TFs) relevant to fibrosis by profiling the matched transcriptomes and epigenomes of 108 human liver biopsies from a deeply-characterized cohort of patients spanning the full histopathologic spectrum of NAFLD. CRISPR-based genetic knockout of the top 100 TFs identified ZNF469 as a regulator of collagen expression in primary human hepatic stellate cells (HSCs). Gain- and loss-of-function studies established that ZNF469 regulates collagen genes and genes involved in matrix homeostasis through direct binding to gene bodies and regulatory elements. By integrating multiomic large-scale profiling of human biopsies with extensive experimental validation we demonstrate that ZNF469 is a transcriptional regulator of collagen in HSCs. Overall, these data nominate ZNF469 as a previously unrecognized determinant of NAFLD-associated liver fibrosis.

Integrative analysis of chromatin accessibility and transcriptome landscapes in the induction of peritoneal fibrosis by high glucose

BACKGROUND

Peritoneal fibrosis is the prevailing complication induced by prolonged exposure to high glucose in patients undergoing peritoneal dialysis.

METHODS

To elucidate the molecular mechanisms underlying this process, we conducted an integrated analysis of the transcriptome and chromatin accessibility profiles of human peritoneal mesothelial cells (HMrSV5) during high-glucose treatment.

RESULTS

Our study identified 2775 differentially expressed genes (DEGs) related to high glucose-triggered pathological changes, including 1164 upregulated and 1611 downregulated genes. Genome-wide DEGs and network analysis revealed enrichment in the epithelial-mesenchymal transition (EMT), inflammatory response, hypoxia, and TGF-beta pathways. The enriched genes included VEGFA, HIF-1α, TGF-β1, EGF, TWIST2, and SNAI2. Using ATAC-seq, we identified 942 hyper (higher ATAC-seq signal in high glucose-treated HMrSV5 cells than in control cells) and 714 hypo (lower ATAC-seq signal in high glucose-treated HMrSV5 cells versus control cells) peaks with differential accessibility in high glucose-treated HMrSV5 cells versus controls. These differentially accessible regions were positively correlated (R = 0.934) with the nearest DEGs. These genes were associated with 566 up- and 398 downregulated genes, including SNAI2, TGF-β1, HIF-1α, FGF2, VEGFA, and VEGFC, which are involved in critical pathways identified by transcriptome analysis. Integrated ATAC-seq and RNA-seq analysis also revealed key transcription factors (TFs), such as HIF-1α, ARNTL, ELF1, SMAD3 and XBP1. Importantly, we demonstrated that HIF-1α is involved in the regulation of several key genes associated with EMT and the TGF-beta pathway. Notably, we predicted and experimentally validated that HIF-1α can exacerbate the expression of TGF-β1 in a high glucose-dependent manner, revealing a novel role of HIF-1α in high glucose-induced pathological changes in human peritoneal mesothelial cells (HPMCs).

CONCLUSIONS

In summary, our study provides a comprehensive view of the role of transcriptome deregulation and chromosome accessibility alterations in high glucose-induced pathological fibrotic changes in HPMCs. This analysis identified hub genes, signaling pathways, and key transcription factors involved in peritoneal fibrosis and highlighted the novel glucose-dependent regulation of TGF-β1 by HIF-1α. This integrated approach has offered a deeper understanding of the pathogenesis of peritoneal fibrosis and has indicated potential therapeutic targets for intervention.

SinCMat: A single-cell-based method for predicting functional maturation transcription factors

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

Distinct epicardial gene regulatory programs drive development and regeneration of the zebrafish heart

Unlike the adult mammalian heart, which has limited regenerative capacity, the zebrafish heart fully regenerates following injury. Reactivation of cardiac developmental programs is considered key to successfully regenerating the heart, yet the regulation underlying the response to injury remains elusive. Here, we compared the transcriptome and epigenome of the developing and regenerating zebrafish epicardia. We identified epicardial enhancer elements with specific activity during development or during adult heart regeneration. By generating gene regulatory networks associated with epicardial development and regeneration, we inferred genetic programs driving each of these processes, which were largely distinct. Loss of Hif1ab, Nrf1, Tbx2b, and Zbtb7a, central regulators of the regenerating epicardial network, in injured hearts resulted in elevated epicardial cell numbers infiltrating the wound and excess fibrosis after cryoinjury. Our work identifies differences between the regulatory blueprint deployed during epicardial development and regeneration, underlining that heart regeneration goes beyond the reactivation of developmental programs.

Detection of new pioneer transcription factors as cell-type-specific nucleosome binders

Wrapping of DNA into nucleosomes restricts accessibility to DNA and may affect the recognition of binding motifs by transcription factors. A certain class of transcription factors, the pioneer transcription factors, can specifically recognize their DNA binding sites on nucleosomes, initiate local chromatin opening, and facilitate the binding of co-factors in a cell-type-specific manner. For the majority of human pioneer transcription factors, the locations of their binding sites, mechanisms of binding, and regulation remain unknown. We have developed a computational method to predict the cell-type-specific ability of transcription factors to bind nucleosomes by integrating ChIP-seq, MNase-seq, and DNase-seq data with details of nucleosome structure. We have demonstrated the ability of our approach in discriminating pioneer from canonical transcription factors and predicted new potential pioneer transcription factors in H1, K562, HepG2, and HeLa-S3 cell lines. Last, we systematically analyzed the interaction modes between various pioneer transcription factors and detected several clusters of distinctive binding sites on nucleosomal DNA.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Detection of new pioneer transcription factors as cell-type specific nucleosome binders

Wrapping of DNA into nucleosomes restricts accessibility to the DNA and may affect the recognition of binding motifs by transcription factors. A certain class of transcription factors, the pioneer transcription factors, can specifically recognize their DNA binding sites on nucleosomes, may initiate local chromatin opening and facilitate the binding of co-factors in a cell-type-specific manner. For the majority of human pioneer transcription factors, the locations of their binding sites, mechanisms of binding and regulation remain unknown. We have developed a computational method to predict the cell-type-specific ability of transcription factors to bind nucleosomes by integrating ChIP-seq, MNase-seq and DNase-seq data with details of nucleosome structure. We have demonstrated the ability of our approach in discriminating pioneer from canonical transcription factors and predicted new potential pioneer transcription factors in H1, K562, HepG2 and HeLa cell lines. Lastly, we systemically analyzed the interaction modes between various pioneer transcription factors and detected several clusters of distinctive binding sites on nucleosomal DNA.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Understanding blood development and leukemia using sequencing-based technologies and human cell systems

Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.

Identification of the regulatory circuit governing corneal epithelial fate determination and disease

The transparent corneal epithelium in the eye is maintained through the homeostasis regulated by limbal stem cells (LSCs), while the nontransparent epidermis relies on epidermal keratinocytes for renewal. Despite their cellular similarities, the precise cell fates of these two types of epithelial stem cells, which give rise to functionally distinct epithelia, remain unknown. We performed a multi-omics analysis of human LSCs from the cornea and keratinocytes from the epidermis and characterized their molecular signatures, highlighting their similarities and differences. Through gene regulatory network analyses, we identified shared and cell type-specific transcription factors (TFs) that define specific cell fates and established their regulatory hierarchy. Single-cell RNA-seq (scRNA-seq) analyses of the cornea and the epidermis confirmed these shared and cell type-specific TFs. Notably, the shared and LSC-specific TFs can cooperatively target genes associated with corneal opacity. Importantly, we discovered that FOSL2, a direct PAX6 target gene, is a novel candidate associated with corneal opacity, and it regulates genes implicated in corneal diseases. By characterizing molecular signatures, our study unveils the regulatory circuitry governing the LSC fate and its association with corneal opacity.

MYCT1 in cancer development: Gene structure, regulation, and biological implications for diagnosis and treatment

Myc target 1 (MYCT1), located at 6q25.2, is a crucial player in cancer development. While widely distributed in cells, its subcellular localization varies across different cancer types. As a novel c-Myc target gene, MYCT1 is subject to regulation by multiple transcription factors. Studies have revealed aberrant expression of MYCT1 in various cancers, impacting pivotal biological processes such as proliferation, apoptosis, migration, genomic instability, and differentiation in cancer cells. Additionally, MYCT1 plays a critical role in modulating tumor angiogenesis and remodeling tumor immune responses within the tumor microenvironment. Despite certain debated functions, MYCT1 undeniably holds significance in cancer development. In this review, we comprehensively examine the relationship between MYCT1 and cancer, encompassing gene structure, regulation of gene expression, gene mutation, and biological function, with the aim of providing valuable insights for cancer diagnosis and treatment.

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

Inducible MLL-AF9 Expression Drives an AML Program during Human Pluripotent Stem Cell-Derived Hematopoietic Differentiation

A t(9;11)(p22;q23) translocation produces the MLL-AF9 fusion protein, which is found in up to 25% of de novo AML cases in children. Despite major advances, obtaining a comprehensive understanding of context-dependent MLL-AF9-mediated gene programs during early hematopoiesis is challenging. Here, we generated a human inducible pluripotent stem cell (hiPSC) model with a doxycycline dose-dependent MLL-AF9 expression. We exploited MLL-AF9 expression as an oncogenic hit to uncover epigenetic and transcriptomic effects on iPSC-derived hematopoietic development and the transformation into (pre-)leukemic states. In doing so, we observed a disruption in early myelomonocytic development. Accordingly, we identified gene profiles that were consistent with primary MLL-AF9 AML and uncovered high-confidence MLL-AF9-associated core genes that are faithfully represented in primary MLL-AF9 AML, including known and presently unknown factors. Using single-cell RNA-sequencing, we identified an increase of CD34 expressing early hematopoietic progenitor-like cell states as well as granulocyte-monocyte progenitor-like cells upon MLL-AF9 activation. Our system allows for careful chemically controlled and stepwise in vitro hiPSC-derived differentiation under serum-free and feeder-free conditions. For a disease that currently lacks effective precision medicine, our system provides a novel entry-point into exploring potential novel targets for personalized therapeutic strategies.

Analysis of the P. lividus sea urchin genome highlights contrasting trends of genomic and regulatory evolution in deuterostomes

Sea urchins are emblematic models in developmental biology and display several characteristics that set them apart from other deuterostomes. To uncover the genomic cues that may underlie these specificities, we generated a chromosome-scale genome assembly for the sea urchin Paracentrotus lividus and an extensive gene expression and epigenetic profiles of its embryonic development. We found that, unlike vertebrates, sea urchins retained ancestral chromosomal linkages but underwent very fast intrachromosomal gene order mixing. We identified a burst of gene duplication in the echinoid lineage and showed that some of these expanded genes have been recruited in novel structures (water vascular system, Aristotle's lantern, and skeletogenic micromere lineage). Finally, we identified gene-regulatory modules conserved between sea urchins and chordates. Our results suggest that gene-regulatory networks controlling development can be conserved despite extensive gene order rearrangement.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

scANANSE gene regulatory network and motif analysis of single-cell clusters

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

ONECUT2 regulates RANKL-dependent enterocyte and microfold cell differentiation in the small intestine; a multi-omics study

Microfold (M) cells reside in the intestinal epithelium of Peyer's patches (PP). Their unique ability to take up and transport antigens from the intestinal lumen to the underlying lymphoid tissue is key in the regulation of the gut-associated immune response. Here, we applied a multi-omics approach to investigate the molecular mechanisms that drive M cell differentiation in mouse small intestinal organoids. We generated a comprehensive profile of chromatin accessibility changes and transcription factor dynamics during in vitro M cell differentiation, allowing us to uncover numerous cell type-specific regulatory elements and associated transcription factors. By using single-cell RNA sequencing, we identified an enterocyte and M cell precursor population. We used our newly developed computational tool SCEPIA to link precursor cell-specific gene expression to transcription factor motif activity in cis-regulatory elements, uncovering high expression of and motif activity for the transcription factor ONECUT2. Subsequent in vitro and in vivo perturbation experiments revealed that ONECUT2 acts downstream of the RANK/RANKL signalling axis to support enterocyte differentiation, thereby restricting M cell lineage specification. This study sheds new light on the mechanism regulating cell fate balance in the PP, and it provides a powerful blueprint for investigation of cell fate switches in the intestinal epithelium.

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era

Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

NETISCE: a network-based tool for cell fate reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

Transcription factors: Bridge between cell signaling and gene regulation

Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional program. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications (PTMs) of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multi-omics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.